Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study

2026-02-19

AI Summary

Existing uncertainty quantification methods struggle to effectively detect hallucinations in long-form text generation. This work proposes the first fine-grained uncertainty quantification framework tailored for long texts, which systematically evaluates generated content through a three-stage pipeline: response decomposition, unit-level scoring, and response aggregation. The framework introduces a unified taxonomy that integrates and extends existing black-box consistency-based methods, enabling fair comparison and modular component selection. It employs black-box consistency scorers—including claim–response entailment, claim-level, and sentence-level scoring—combined with uncertainty-aware decoding. Experimental results demonstrate that claim–response entailment scoring achieves the best performance, claim-level scoring outperforms sentence-level scoring, and uncertainty-aware decoding significantly enhances factual consistency in long-form generation.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Uncertainty quantification has emerged as an effective approach to closed-book hallucination detection for LLMs, but existing methods are largely designed for short-form outputs and do not generalize well to long-form generation. We introduce a taxonomy for fine-grained uncertainty quantification in long-form LLM outputs that distinguishes methods by design choices at three stages: response decomposition, unit-level scoring, and response-level aggregation. We formalize several families of consistency-based black-box scorers, providing generalizations and extensions of existing methods. In our experiments across multiple LLMs and datasets, we find 1) claim-response entailment consistently performs better or on par with more complex claim-level scorers, 2) claim-level scoring generally yields better results than sentence-level scoring, and 3) uncertainty-aware decoding is highly effective for improving the factuality of long-form outputs. Our framework clarifies relationships between prior methods, enables apples-to-apples comparisons, and provides practical guidance for selecting components for fine-grained UQ.

Problem

• uncertainty quantification
• long-form generation
• hallucination detection
• language models
• fine-grained

Innovation

• uncertainty quantification
• long-form generation
• fine-grained scoring
• claim-level entailment
• uncertainty-aware decoding